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Computation of Ocean Heat Content, Ocean Mean Temperature of 7 Layers on Operational Basis

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Computation of Ocean Heat Content, Ocean Mean Temperature of 7 Layers on Operational Basis Technical Report NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 Computation of Ocean Heat Content, Ocean Mean Temperature of 7 layers on Operational basis Geophysical Products Development Division Geophysical and Special Processing Group Satellite Data Acquisition & Products Services Area National Remote Sensing Centre (ISRO) Balanagar, Hyderabad. Dec. 2014 NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 NATIONAL REMOTE SENSING CENTRE DOCUMENT CONTROL SHEET 1. Security classification Unclassified 2. Distribution Through soft and hard copies 3. Report/Document version (a) Issue: 01 (b) Date: 10-12-2014 4. Report/Document Type Technical Report 5. Document Control No NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 Computation of Ocean Heat Content, Ocean Mean 6. Title Temperature of 7 layers on operational basis 7. Particulars of Collation Pages: 9 Figures:1 Tables: 1 References: 8 1 1 2 8. Author(s) M. Suresh Kumar , A. Senthil Kumar and MM Ali 1-GPDD, G&SP,SDAPSA, NRSC, ISRO 9. Affiliation of authors 2-Director's Office, NRSC, ISRO Compiled by Reviewed by Approved by 10. Scrutiny mechanism GPDD GD, G&SP GD, G&SP 11. Originating unit GPDD / G&SPG / SDAPSA / NRSC Sponsor(s)/Name and NRSC, ISRO, Govt. of India 12. Address 13. Date of Project Initiation July, 2014 14. Date of Publication Dec, 2014 Abstract: Ocean heat content (OHC) and Ocean Mean Temperature (OMT) are important climatic parameters required for atmospheric and oceanic studies like cyclone and monsoon predictions and ocean heat transport estimations. The data used to estimate these parameters are (a) sea surface height anomaly (SSHA) from the available altimeters, (b) sea surface temperature (SST) from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the climatological values 15. of OHC and OMT of various depths (50, 100, 150, 200, 300, 500, 700m and TCHP as an integral of OHC from surface to 26oc isotherm and it's mean temperature). These parameters are estimated on a daily basis from 1998 to present with a delay of 3 days using artificial neural network techniques. The estimated OHC and OMT values are validated with independent data set and are found to be significantly correlated with the observed values. These products are made freely available and downloadable at http://bhuvan.nrsc.gov.in/data/download/index.php Page 2 of 9 NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 COMPUTATION OF OCEAN HEAT CONTENT, OCEAN MEAN TEMPERATU RE OF 7 LAYERS ON OPERATIONAL BASIS 1. Abstract Ocean heat content (OHC) and Ocean Mean Temperature (OMT) are important climatic parameters required for atmospheric and oceanic studies like cyclone and monsoon predictions and ocean heat transport estimations. The data used to estimate these parameters are (a) sea surface height anomaly (SSHA) from the available altimeters, (b) sea surface temperature (SST) from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the climatological values of OHC and OMT of various depths (50, 100, 150, 200, 300, 500, 700m and TCHP as an integral of OHC from surface to 26oc isotherm and it's mean temperature). These parameters are estimated on a daily basis from 1998 to present with a delay of 3 days using artificial neural network techniques. The estimated OHC, OMT values are validated with independent data set and are found to be significantly correlated with the observed values. These products are made freely available and downloadable at http://bhuvan.nrsc.gov.in/data/download/index.php 2. Introduction Ocean thermal energy is one of the key factors fueling the genesis and propagation of cyclones. Prediction of tropical cyclone intensity has been a challenging problem. Utilization of this thermal energy to improve the atmospheric models for cyclone and monsoon predictions has been realized in recent years [8]. Ocean Heat Content (OHC), an important ocean climatic parameter in determining the heat flux in the ocean-atmosphere system, can influence the weather systems like cyclones and monsoons. Hence, a precise estimate of OHC is essential for understanding the role of oceans in assessing the past and future climate change. Heat content of a slice of the ocean can be estimated as a product of integrated temperature, density of sea water and specific heat capacity from the surface down to a required depth. It is obtained by summing the heat content of the ocean column from the sea surface to a particular depth. OHC can be computed from in situ measurements from the equation (1). OHC C h1 TdZ p h 2 ……………………………………………….(1) Page 3 of 9 NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 Where ρ is the density of the sea water, Cp is the specific heat capacity of the sea water at constant pressure, p; h1 is top depth, h2 is bottom depth and T is the temperature in oC. The best approach for computing OHC is to use the in situ measurements, but due to the limited availability of in situ temperature profiles in space and time, remotely sensed sea surface temperature (SST) and sea surface height anomalies (SSHA) are employed in the computation of OHC in the Indian Ocean. OHC derived from in situ temperature profiles from ARGO floats along with collocated SST, SSHA and OHC climatology are generally used to estimate OHC values at various depths using an artificial neural network model [1]. Using this approach, these parameters are estimated daily on near real time basis. An example temperature profile shown in below Fig [1]. As the depth increases the ocean temperature decreases. The Argo program measures the temperature and salinity of ocean surface waters around the world. It has deployed 3,000 free-drifting floats all over the ocean to measure the salinity and temperature throughout the surface layer of the ocean. Each float is programmed to sink to 2,000 meters down, drifting at that depth for about 10 days. The float then makes its way to the surface measuring temperature and salinity while popping up. Data is transmitted through the satellite communication once the float reaches the surface. Temperature profiles are also available from XBT (eXpendable Bathy Thermograph) and CTD (Conductivity, Temperature, Depth). Fig. 1. Simple temperature-depth ocean water profile The SST is the skin temperature of a very thin layer of about a few micrometres of the ocean. The SST has direct interaction with atmosphere. This has been the only oceanographic parameter used to represent the ocean heat energy. However, tropical cyclones and monsoons have long been known Page 4 of 9 NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 to interact with the deeper layers of the ocean than sea surface represented by the SST alone. The SSHA provides an integrated picture of ocean from bottom to surface. Typically, positive (negative) SSHAs correspond to more (less) upper OHC/OMT. Such information has been used to study tropical cyclones [5]. Since the SSHA is strongly correlated with the thermal structure of the upper ocean, the OHC can be estimated from this parameter over finer spatial and temporal scales on an operational basis. Ali et al. (2012) suggested a better method of estimating tropical cyclone heat potential from SSHA and sea surface temperature (SST) using a neural network approach [1]. Here, we use a similar approach. The only difference is (i) heat content, mean temperature values are made available up to various depths 50m, 100m, 150m, 200m, 300m, 500, 700m and surface to 26oC isotherm instead of providing heat content values only up to 700m [3][1] . We have estimated at various depths because as of now we do not have a database information on which layer of the ocean interacts with different atmosphere process. The ANN is a massive parallel-distributed computer model consisting of simple processing units called artificial neurons which are the basic functioning units. The ANN has been widely used in various meteorological, oceanographic, atmospheric studies and satellite remote sensing retrievals [6] [7]. In the present analysis, we used multilayer perceptions, which are feed-forward neural networks, with one input layer, three hidden layers and a one output layer. The input (independent) parameters are SSHA, SST and OHCclim / OMTclim. Dependent parameter is the OHCin situ / OMT in situ The data are available on near real time basis for a researcher to download from the NRSC Bhuvan o o o o website from 1998 onward over the north Indian Ocean spanning 0 N - 30 N and 40 E - 120 E. 3. Data and Methodology Following data have been used in this ANN approach for estimating OHC, OMT: SSHA: Aviso (Archiving, Validation and Interpretation of Satellite Oceanographic data) distributes satellite altimetry data from Topex/Poseidon, Jason-1, ERS-1 and ERS-2, and EnviSat and Geosat Follow On (GFO) products worldwide since 1992. Since launches of new altimetric mission, series of products has been completing the Aviso catalogue. Ssalto/Duacs Gridded Sea level anomalies (1/3°x1/3° on a Mercator grid) is a gridded SSHA computed with respect to a 7-years mean [1993 to 1999] and are provided in near-real-time and in delayed time bases. Recently aviso(v15.0) updated the ssha files at the resolution of 0.25° as a reference Page 5 of 9 NRSC-SDAPSA-G&SPG-DEC-2014-TR-672 mean of 20 years [1993 to 2012] period.(http://www.aviso.altimetry.fr/en/data/products/sea- surface-height-products/global/ssha.html) Corrective steps are taken in SSHA data to ensure this consistency between the Map of Sea Level Anomaly (MSLA) and Mean Dynamic Topography (MDT) products. The obtained MSLA is equivalent to the former DUACS products by changing the MSLA reference period using Equation-2 SLA_7y = SLA_20y – ref20yto7y ………………………………. (2) Where SLA_7y is the seven years mean, SLA_20y is the mean of 20 years and ref20yto7y is the Change of reference from 20 years to 7 years.
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